Tracking using explanation-based modeling

TL;DR

This paper introduces an explanation-based framework for tracking that is more robust to model mismatches than traditional Bayesian methods, demonstrated through improved performance on simulated data.

Contribution

The paper proposes a novel explanatory framework for tracking, along with an efficient algorithm that outperforms Bayesian methods under model mismatches.

Findings

Our algorithm outperforms Bayesian tracking with slight model mismatches.

Experimental results show significant robustness improvements.

The approach is validated on simulated data.

Abstract

We study the tracking problem, namely, estimating the hidden state of an object over time, from unreliable and noisy measurements. The standard framework for the tracking problem is the generative framework, which is the basis of solutions such as the Bayesian algorithm and its approximation, the particle filters. However, the problem with these solutions is that they are very sensitive to model mismatches. In this paper, motivated by online learning, we introduce a new framework -- an {\em explanatory} framework -- for tracking. We provide an efficient tracking algorithm for this framework. We provide experimental results comparing our algorithm to the Bayesian algorithm on simulated data. Our experiments show that when there are slight model mismatches, our algorithm vastly outperforms the Bayesian algorithm.